Call for Challenge on Fine-Grained Sentiment Analysis @ ESWC2016.
====================================================================================================
Venue: Crete, Greece
Hashtag: #SentimentAnalysis
Conference Site: http://2016.eswc-conferences.org/
Challenge Site: https://github.com/diegoref/SSA2016
Easychair Submission Page:
https://easychair.org/conferences/?conf=emsasw2016
====================================================================================================
Social media evolution has given users one important opportunity for
expressing, sharing and commenting their thoughts and opinions online. The
information thus produced is related to many different areas such as
commerce, tourism, education, health and causes the size of the Social Web
to expand exponentially.
There is a great opportunity that arises from this amount of information
which is the one to automatically detect and mine the opinions of the users.
This has raised further interest within the scientific community where open
challenges still exist and the business world where social analysis brings
substantial benefits. According to an IDC survey, the amount of
unstructured text occupies 80% of the digital space with respect to the 20%
of the structured text. Besides, there are not so many solutions that can
accurately analyse the text and present insights in an understandable
manner as this task is still extremely difficult. In fact, mining opinions
and sentiments from natural language involves a deep understanding of most
of the explicit and implicit, regular and irregular, syntactical and
semantic rules proper of a language.
Existing approaches are mainly focused on the identification of parts of
the text where opinions and sentiments can be explicitly expressed such as
polarity terms, expressions, affect words. They usually adopt purely
syntactical approaches and are heavily dependant on the source language of
the input text. It follows that they miss many language patterns where
opinions can be expressed because this would involve a deep analysis of the
semantics of a sentence.
One example is constituted by the presence of implicit opinions deriving
from particular use of verbs that are difficult to catch using a classical
sentiment analysis tool.
As an example, a sentence such as Players of that soccer team are happy
that the president has fired the coach includes the verb are that carries a
positive sentiment tone happy of the expressed opinion. However, this is
not enough for a complete sentiment analysis of this sentence. With
classical sentiment analysis approaches we can only state that the sentence
expresses a positive opinion on the event has fired and nothing else. With
fine-grained sentiment analysis we can go one step deeper as it focuses on
a semantic analysis of text through the use of web ontologies, semantic
resources, or semantic networks, allowing the identification of opinion
data which with only natural language techniques would be very difficult.
Fine-Grained sentiment analysis allows, in the example above, detecting a
negative opinions of the holder Players towards the subject coach.
Understanding the semantics of a sentence offers an exciting research
opportunity and challenge to the Semantic Web community as well. In fact,
the Fine-Grained Sentiment Analysis Challenge aims to go beyond a mere
word-level analysis of text and provides novel methods to opinion mining
and sentiment analysis that can transform more efficiently unstructured
textual information to structured machine-processable data, in potentially
any domain.
By relying on large semantic knowledge bases and Semantic Web best
practices and techniques, fine-grained sentiment analysis steps away from
blind use of keywords, simple statistical analysis based on syntactical
rules, but rather relies on the implicit features associated with natural
language concepts. Unlike purely syntactical techniques, semantic sentiment
analysis approaches are able to detect also sentiments that are implicitly
expressed within the text.
*** Submissions ***
Two steps submission
* First step:
1. Abstract: no more than 200 words.
2. Paper (max 4 pages): containing the details of the system, including why
the system is innovative, which features or functions the system provides,
what design choices were made and what lessons were learned, how the
semantics has been employed and which tasks the system addresses.
* Second step (for accepted systems only)
1. Paper (max 15 pages): full description of the submitted system.
2. Web Access: applications should be either accessible via the web or
downloadable. If an application is not publicly accessible, password must
be provided for reviewers. A short set of instructions on how to use the
application should be provided as well.
3. If accepted, the authors will have the possibility to present a poster
and a demo advertising their work in a dedicated networking session.
Papers must comply with the LNCS style
Papers are submitted in PDF format via the workshop’s EasyChair submission
pages: https://easychair.org/conferences/?conf=emsasw2016
Accepted papers will be published by Springer.
Extended versions of best systems will be invited to journal special issues.
All the participants are invited to submit a paper containing the research
aspects of their systems to the ESWC 2016 Workshop on Emotions, Modality,
Sentiment Analysis and the Semantic Web (
http://www.maurodragoni.com/research/opinionmining/events/)
*** Important dates ***
March 21th, 2016, 23:59 CET: First step submission
March 25th, 2016, 23:59 CET: Notification of acceptance
April 29th, 2016, 23:59 CET: Second step submission
May 29 - June 2, 2016: ESWC 2016 Challenge days
*** Workshop Chairs ***
Prof. Diego Reforgiato Recupero
Dr. Mauro Dragoni
--
Dr. Mauro Dragoni
Post-Doc Researcher at Fondazione Bruno Kessler (FBK-IRST)
Via Sommarive 18, 38123, Povo, Trento, Italy
Tel. 0461-314053